About Me
I am a Research Scientist at OpenAI, where I work on large-scale language model (LLM) training. Before that, my research focuses on building self-improving LLMs that can continuously learn from interaction, feedback, and experience. To support this goal, I have extensively studied and applied reinforcement learning (RL) for post-training, reasoning, and agentic behaviors in large-scale models.
I earned my Ph.D. in Computer Science and Engineering from University of Notre Dame in 2023, advised by Prof. Meng Jiang . My research during Ph.D. was generously supported by the Bloomberg Ph.D Fellowship . I also enjoyed amazing internship experiences at Microsoft Research, AI2, and Bloomberg.
What's New
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- Jan 2026 Four papers have been accepted at ICLR 2026, covering topics of self-improving LLMs and parallel reasoning.
- Aug 2025 One paper has been accepted at EMNLP 2025 on self-evolving agent.
Selected Publications
For a full list of publications, please refer to my Google Scholar page
Industry Experience
Mentoring
I’ve been fortunate to mentor and work alongside many talented students:
- Chengsong Huang (2025), WUSTL, advised by Prof. Jiaxin Huang. Topic: Self-improving LLM [R-Zero]
- Shangbin Feng (2025), UW at Seattle, advised by Prof. Yulia Tsvetkov. Topic: Multi-Agent [SwitcherLM]
- Zongxia Li (2025), UMD, advised by Prof. Jordan Boyd-Graber. Topic: Self-improving LLM [Vision-SR1]
- Siru Ouyang (2024), UIUC, advised by Prof. Jiawei Han. Topic: LLM agent [RepoGraph]
- Mengzhao Jia (2024), UND, advised by Prof. Meng Jiang. Topic: Multi-modal [Leopard]
- Tong Chen (2023), UW at Seattle, advised by Prof. Luke Zettlemoyer Topic: RAG [Dense X Retrieval]